Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f27d47a8d30>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f27bdebd278>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), "input_real")
    input_z = tf.placeholder(tf.float32, (None, z_dim), "input_z")
    learning_rate = tf.placeholder(tf.float32, None, "learning_rate")

    return input_real, input_z, learning_rate

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)

        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha=0.2
    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 2*2*512)
        
        x1 = tf.reshape(x1, (-1,2,2,512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        out = tf.tanh(logits)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    alpha=0.2
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    _, width, height, channels = data_shape
    input_real, input_z, learn_rate = model_inputs(width, height, channels, z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    print_every = 20
    show_every = 100
    losses = []
    n_images = 16
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                batch_images *= 2
                steps += 1
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})

                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                if steps % show_every == 0:
                    show_generator_output(sess, n_images, input_z, channels, data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 64
z_dim = 100
learning_rate = 0.001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 0/2... Discriminator Loss: 4.5889... Generator Loss: 0.0539
Epoch 0/2... Discriminator Loss: 1.7654... Generator Loss: 7.3830
Epoch 0/2... Discriminator Loss: 0.7816... Generator Loss: 4.2150
Epoch 0/2... Discriminator Loss: 0.3470... Generator Loss: 1.8844
Epoch 0/2... Discriminator Loss: 0.4269... Generator Loss: 2.1760
Epoch 0/2... Discriminator Loss: 0.5913... Generator Loss: 1.2102
Epoch 0/2... Discriminator Loss: 2.6551... Generator Loss: 0.1700
Epoch 0/2... Discriminator Loss: 0.0290... Generator Loss: 5.7910
Epoch 0/2... Discriminator Loss: 0.4118... Generator Loss: 2.2551
Epoch 0/2... Discriminator Loss: 0.3948... Generator Loss: 1.6926
Epoch 0/2... Discriminator Loss: 0.9048... Generator Loss: 5.8853
Epoch 0/2... Discriminator Loss: 0.3234... Generator Loss: 2.2264
Epoch 0/2... Discriminator Loss: 1.4985... Generator Loss: 0.4945
Epoch 0/2... Discriminator Loss: 0.2333... Generator Loss: 3.5330
Epoch 0/2... Discriminator Loss: 0.4490... Generator Loss: 2.9282
Epoch 0/2... Discriminator Loss: 0.3193... Generator Loss: 2.6473
Epoch 0/2... Discriminator Loss: 0.2527... Generator Loss: 2.5836
Epoch 0/2... Discriminator Loss: 0.7749... Generator Loss: 1.2233
Epoch 0/2... Discriminator Loss: 0.6444... Generator Loss: 1.6025
Epoch 0/2... Discriminator Loss: 0.7556... Generator Loss: 1.2016
Epoch 0/2... Discriminator Loss: 0.7623... Generator Loss: 1.0556
Epoch 0/2... Discriminator Loss: 0.5610... Generator Loss: 1.3225
Epoch 0/2... Discriminator Loss: 0.6432... Generator Loss: 1.6614
Epoch 0/2... Discriminator Loss: 0.6372... Generator Loss: 2.7996
Epoch 0/2... Discriminator Loss: 1.1542... Generator Loss: 0.5667
Epoch 0/2... Discriminator Loss: 1.1081... Generator Loss: 3.2024
Epoch 0/2... Discriminator Loss: 0.7643... Generator Loss: 1.1818
Epoch 0/2... Discriminator Loss: 0.7947... Generator Loss: 1.6611
Epoch 0/2... Discriminator Loss: 0.8828... Generator Loss: 2.3937
Epoch 0/2... Discriminator Loss: 1.4033... Generator Loss: 3.4885
Epoch 0/2... Discriminator Loss: 0.7125... Generator Loss: 1.1786
Epoch 0/2... Discriminator Loss: 0.5687... Generator Loss: 1.6013
Epoch 0/2... Discriminator Loss: 1.0703... Generator Loss: 0.7689
Epoch 0/2... Discriminator Loss: 1.2667... Generator Loss: 0.6456
Epoch 0/2... Discriminator Loss: 0.8216... Generator Loss: 1.2977
Epoch 0/2... Discriminator Loss: 0.5774... Generator Loss: 1.4200
Epoch 0/2... Discriminator Loss: 0.9998... Generator Loss: 3.0867
Epoch 0/2... Discriminator Loss: 0.6893... Generator Loss: 1.1304
Epoch 0/2... Discriminator Loss: 1.3805... Generator Loss: 0.4889
Epoch 0/2... Discriminator Loss: 1.0081... Generator Loss: 2.0214
Epoch 0/2... Discriminator Loss: 1.0471... Generator Loss: 2.6065
Epoch 0/2... Discriminator Loss: 0.9706... Generator Loss: 0.7389
Epoch 0/2... Discriminator Loss: 1.0107... Generator Loss: 2.8505
Epoch 0/2... Discriminator Loss: 0.9651... Generator Loss: 0.9350
Epoch 0/2... Discriminator Loss: 0.9009... Generator Loss: 0.8863
Epoch 0/2... Discriminator Loss: 0.9955... Generator Loss: 0.7127
Epoch 1/2... Discriminator Loss: 1.2303... Generator Loss: 2.6382
Epoch 1/2... Discriminator Loss: 0.8719... Generator Loss: 0.9767
Epoch 1/2... Discriminator Loss: 1.0155... Generator Loss: 0.8074
Epoch 1/2... Discriminator Loss: 1.1981... Generator Loss: 0.5833
Epoch 1/2... Discriminator Loss: 0.8559... Generator Loss: 0.8563
Epoch 1/2... Discriminator Loss: 1.0420... Generator Loss: 0.7247
Epoch 1/2... Discriminator Loss: 1.0335... Generator Loss: 2.4035
Epoch 1/2... Discriminator Loss: 0.7861... Generator Loss: 1.1182
Epoch 1/2... Discriminator Loss: 0.9037... Generator Loss: 1.8706
Epoch 1/2... Discriminator Loss: 1.0159... Generator Loss: 0.6487
Epoch 1/2... Discriminator Loss: 2.8712... Generator Loss: 5.3416
Epoch 1/2... Discriminator Loss: 1.2713... Generator Loss: 0.5168
Epoch 1/2... Discriminator Loss: 0.8580... Generator Loss: 1.0348
Epoch 1/2... Discriminator Loss: 2.7322... Generator Loss: 0.1240
Epoch 1/2... Discriminator Loss: 0.9050... Generator Loss: 0.7870
Epoch 1/2... Discriminator Loss: 1.4734... Generator Loss: 2.5375
Epoch 1/2... Discriminator Loss: 1.0012... Generator Loss: 0.7305
Epoch 1/2... Discriminator Loss: 1.0362... Generator Loss: 0.5817
Epoch 1/2... Discriminator Loss: 1.7900... Generator Loss: 3.4536
Epoch 1/2... Discriminator Loss: 0.6314... Generator Loss: 1.7080
Epoch 1/2... Discriminator Loss: 0.8729... Generator Loss: 0.8377
Epoch 1/2... Discriminator Loss: 0.6686... Generator Loss: 1.1268
Epoch 1/2... Discriminator Loss: 1.3878... Generator Loss: 3.4955
Epoch 1/2... Discriminator Loss: 0.5473... Generator Loss: 1.4661
Epoch 1/2... Discriminator Loss: 1.0073... Generator Loss: 1.5708
Epoch 1/2... Discriminator Loss: 0.8551... Generator Loss: 1.6875
Epoch 1/2... Discriminator Loss: 0.7538... Generator Loss: 1.0934
Epoch 1/2... Discriminator Loss: 0.9334... Generator Loss: 0.7987
Epoch 1/2... Discriminator Loss: 0.9954... Generator Loss: 1.5809
Epoch 1/2... Discriminator Loss: 1.4011... Generator Loss: 0.4042
Epoch 1/2... Discriminator Loss: 0.9648... Generator Loss: 0.7732
Epoch 1/2... Discriminator Loss: 0.9162... Generator Loss: 1.7907
Epoch 1/2... Discriminator Loss: 1.2395... Generator Loss: 2.0871
Epoch 1/2... Discriminator Loss: 1.5119... Generator Loss: 2.6941
Epoch 1/2... Discriminator Loss: 1.4023... Generator Loss: 0.4180
Epoch 1/2... Discriminator Loss: 0.7352... Generator Loss: 1.9749
Epoch 1/2... Discriminator Loss: 1.1853... Generator Loss: 0.4985
Epoch 1/2... Discriminator Loss: 2.8944... Generator Loss: 5.0792
Epoch 1/2... Discriminator Loss: 0.8778... Generator Loss: 0.7921
Epoch 1/2... Discriminator Loss: 1.0848... Generator Loss: 0.6263
Epoch 1/2... Discriminator Loss: 0.8558... Generator Loss: 0.9490
Epoch 1/2... Discriminator Loss: 1.0993... Generator Loss: 0.5805
Epoch 1/2... Discriminator Loss: 1.1541... Generator Loss: 0.5280
Epoch 1/2... Discriminator Loss: 0.5781... Generator Loss: 2.5041
Epoch 1/2... Discriminator Loss: 0.7633... Generator Loss: 1.1016
Epoch 1/2... Discriminator Loss: 1.1046... Generator Loss: 0.5731
Epoch 1/2... Discriminator Loss: 0.6871... Generator Loss: 1.2559

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [16]:
batch_size = 32
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 0/1... Discriminator Loss: 4.4342... Generator Loss: 0.0130
Epoch 0/1... Discriminator Loss: 0.5641... Generator Loss: 1.8439
Epoch 0/1... Discriminator Loss: 0.4896... Generator Loss: 7.7048
Epoch 0/1... Discriminator Loss: 1.2099... Generator Loss: 3.5292
Epoch 0/1... Discriminator Loss: 1.1268... Generator Loss: 10.7168
Epoch 0/1... Discriminator Loss: 0.2584... Generator Loss: 2.8458
Epoch 0/1... Discriminator Loss: 0.2566... Generator Loss: 1.8600
Epoch 0/1... Discriminator Loss: 0.3160... Generator Loss: 1.9050
Epoch 0/1... Discriminator Loss: 1.2875... Generator Loss: 8.0683
Epoch 0/1... Discriminator Loss: 0.6611... Generator Loss: 1.1676
Epoch 0/1... Discriminator Loss: 1.5458... Generator Loss: 0.3872
Epoch 0/1... Discriminator Loss: 0.7229... Generator Loss: 1.0176
Epoch 0/1... Discriminator Loss: 0.9685... Generator Loss: 2.0150
Epoch 0/1... Discriminator Loss: 0.7208... Generator Loss: 1.2567
Epoch 0/1... Discriminator Loss: 1.3802... Generator Loss: 1.2691
Epoch 0/1... Discriminator Loss: 0.7233... Generator Loss: 1.0501
Epoch 0/1... Discriminator Loss: 0.7833... Generator Loss: 1.3734
Epoch 0/1... Discriminator Loss: 1.3093... Generator Loss: 0.7390
Epoch 0/1... Discriminator Loss: 1.0883... Generator Loss: 2.3695
Epoch 0/1... Discriminator Loss: 0.3889... Generator Loss: 1.6333
Epoch 0/1... Discriminator Loss: 0.8504... Generator Loss: 0.7599
Epoch 0/1... Discriminator Loss: 0.8192... Generator Loss: 0.9050
Epoch 0/1... Discriminator Loss: 0.8159... Generator Loss: 0.8805
Epoch 0/1... Discriminator Loss: 0.9262... Generator Loss: 1.4074
Epoch 0/1... Discriminator Loss: 0.5188... Generator Loss: 2.5766
Epoch 0/1... Discriminator Loss: 0.9210... Generator Loss: 0.9062
Epoch 0/1... Discriminator Loss: 0.8404... Generator Loss: 0.9733
Epoch 0/1... Discriminator Loss: 0.9968... Generator Loss: 0.9705
Epoch 0/1... Discriminator Loss: 1.0727... Generator Loss: 1.3948
Epoch 0/1... Discriminator Loss: 1.0180... Generator Loss: 1.4147
Epoch 0/1... Discriminator Loss: 0.7276... Generator Loss: 1.4269
Epoch 0/1... Discriminator Loss: 1.2586... Generator Loss: 0.6194
Epoch 0/1... Discriminator Loss: 0.8895... Generator Loss: 1.2054
Epoch 0/1... Discriminator Loss: 1.4939... Generator Loss: 0.6260
Epoch 0/1... Discriminator Loss: 1.0880... Generator Loss: 1.4315
Epoch 0/1... Discriminator Loss: 1.0754... Generator Loss: 1.2155
Epoch 0/1... Discriminator Loss: 0.8813... Generator Loss: 1.1592
Epoch 0/1... Discriminator Loss: 1.0037... Generator Loss: 0.8609
Epoch 0/1... Discriminator Loss: 0.8594... Generator Loss: 1.6297
Epoch 0/1... Discriminator Loss: 1.0014... Generator Loss: 1.3859
Epoch 0/1... Discriminator Loss: 0.6738... Generator Loss: 1.4583
Epoch 0/1... Discriminator Loss: 0.8090... Generator Loss: 0.9850
Epoch 0/1... Discriminator Loss: 1.3359... Generator Loss: 0.5483
Epoch 0/1... Discriminator Loss: 1.0929... Generator Loss: 2.1035
Epoch 0/1... Discriminator Loss: 0.9052... Generator Loss: 1.2329
Epoch 0/1... Discriminator Loss: 0.8356... Generator Loss: 1.1507
Epoch 0/1... Discriminator Loss: 1.0497... Generator Loss: 2.4629
Epoch 0/1... Discriminator Loss: 1.0038... Generator Loss: 0.9131
Epoch 0/1... Discriminator Loss: 0.8654... Generator Loss: 1.0760
Epoch 0/1... Discriminator Loss: 1.2856... Generator Loss: 0.4562
Epoch 0/1... Discriminator Loss: 1.0074... Generator Loss: 1.9048
Epoch 0/1... Discriminator Loss: 0.9124... Generator Loss: 1.0670
Epoch 0/1... Discriminator Loss: 1.0089... Generator Loss: 1.0027
Epoch 0/1... Discriminator Loss: 0.8938... Generator Loss: 1.2550
Epoch 0/1... Discriminator Loss: 1.0724... Generator Loss: 0.7663
Epoch 0/1... Discriminator Loss: 1.0522... Generator Loss: 0.8895
Epoch 0/1... Discriminator Loss: 1.0631... Generator Loss: 0.7303
Epoch 0/1... Discriminator Loss: 0.8288... Generator Loss: 1.3329
Epoch 0/1... Discriminator Loss: 0.7609... Generator Loss: 0.8739
Epoch 0/1... Discriminator Loss: 0.8277... Generator Loss: 1.0962
Epoch 0/1... Discriminator Loss: 0.9535... Generator Loss: 1.2455
Epoch 0/1... Discriminator Loss: 1.2073... Generator Loss: 0.6046
Epoch 0/1... Discriminator Loss: 0.8432... Generator Loss: 1.9061
Epoch 0/1... Discriminator Loss: 0.7524... Generator Loss: 1.2054
Epoch 0/1... Discriminator Loss: 1.1669... Generator Loss: 0.6262
Epoch 0/1... Discriminator Loss: 1.1834... Generator Loss: 0.7358
Epoch 0/1... Discriminator Loss: 0.8449... Generator Loss: 1.1901
Epoch 0/1... Discriminator Loss: 0.8991... Generator Loss: 0.9033
Epoch 0/1... Discriminator Loss: 0.9619... Generator Loss: 0.8133
Epoch 0/1... Discriminator Loss: 1.0346... Generator Loss: 1.6396
Epoch 0/1... Discriminator Loss: 1.2740... Generator Loss: 1.8823
Epoch 0/1... Discriminator Loss: 1.3215... Generator Loss: 0.6747
Epoch 0/1... Discriminator Loss: 1.0468... Generator Loss: 0.8803
Epoch 0/1... Discriminator Loss: 0.9849... Generator Loss: 1.5511
Epoch 0/1... Discriminator Loss: 1.3419... Generator Loss: 2.0156
Epoch 0/1... Discriminator Loss: 1.0830... Generator Loss: 0.8572
Epoch 0/1... Discriminator Loss: 1.1721... Generator Loss: 1.0592
Epoch 0/1... Discriminator Loss: 0.6210... Generator Loss: 2.1312
Epoch 0/1... Discriminator Loss: 1.2135... Generator Loss: 1.7693
Epoch 0/1... Discriminator Loss: 0.9504... Generator Loss: 1.3468
Epoch 0/1... Discriminator Loss: 1.1431... Generator Loss: 0.6400
Epoch 0/1... Discriminator Loss: 1.3964... Generator Loss: 2.1353
Epoch 0/1... Discriminator Loss: 1.2447... Generator Loss: 0.5054
Epoch 0/1... Discriminator Loss: 1.1720... Generator Loss: 0.5668
Epoch 0/1... Discriminator Loss: 0.9990... Generator Loss: 1.3134
Epoch 0/1... Discriminator Loss: 1.0031... Generator Loss: 1.1906
Epoch 0/1... Discriminator Loss: 1.0093... Generator Loss: 0.6580
Epoch 0/1... Discriminator Loss: 0.9398... Generator Loss: 0.7913
Epoch 0/1... Discriminator Loss: 1.1179... Generator Loss: 0.9881
Epoch 0/1... Discriminator Loss: 0.9538... Generator Loss: 1.2517
Epoch 0/1... Discriminator Loss: 0.8499... Generator Loss: 1.3355
Epoch 0/1... Discriminator Loss: 1.0172... Generator Loss: 1.5291
Epoch 0/1... Discriminator Loss: 0.9775... Generator Loss: 1.0885
Epoch 0/1... Discriminator Loss: 0.9383... Generator Loss: 1.0944
Epoch 0/1... Discriminator Loss: 0.9425... Generator Loss: 0.9313
Epoch 0/1... Discriminator Loss: 1.5369... Generator Loss: 0.3957
Epoch 0/1... Discriminator Loss: 0.8391... Generator Loss: 0.8701
Epoch 0/1... Discriminator Loss: 0.9758... Generator Loss: 1.7652
Epoch 0/1... Discriminator Loss: 0.8410... Generator Loss: 1.4926
Epoch 0/1... Discriminator Loss: 1.0670... Generator Loss: 1.0954
Epoch 0/1... Discriminator Loss: 1.2499... Generator Loss: 0.4762
Epoch 0/1... Discriminator Loss: 1.1029... Generator Loss: 1.7672
Epoch 0/1... Discriminator Loss: 0.9829... Generator Loss: 0.9642
Epoch 0/1... Discriminator Loss: 1.0863... Generator Loss: 0.6897
Epoch 0/1... Discriminator Loss: 1.0469... Generator Loss: 0.6405
Epoch 0/1... Discriminator Loss: 0.9454... Generator Loss: 0.9668
Epoch 0/1... Discriminator Loss: 1.0289... Generator Loss: 1.2051
Epoch 0/1... Discriminator Loss: 0.8326... Generator Loss: 1.2389
Epoch 0/1... Discriminator Loss: 1.0068... Generator Loss: 1.1392
Epoch 0/1... Discriminator Loss: 0.9306... Generator Loss: 0.9845
Epoch 0/1... Discriminator Loss: 0.8683... Generator Loss: 1.1200
Epoch 0/1... Discriminator Loss: 1.2608... Generator Loss: 0.4871
Epoch 0/1... Discriminator Loss: 1.1312... Generator Loss: 0.7105
Epoch 0/1... Discriminator Loss: 1.0984... Generator Loss: 1.2076
Epoch 0/1... Discriminator Loss: 1.0789... Generator Loss: 0.7380
Epoch 0/1... Discriminator Loss: 0.8912... Generator Loss: 1.2114
Epoch 0/1... Discriminator Loss: 1.2613... Generator Loss: 1.7174
Epoch 0/1... Discriminator Loss: 1.0972... Generator Loss: 0.7705
Epoch 0/1... Discriminator Loss: 0.9613... Generator Loss: 1.4482
Epoch 0/1... Discriminator Loss: 0.9679... Generator Loss: 1.4060
Epoch 0/1... Discriminator Loss: 0.8566... Generator Loss: 1.0727
Epoch 0/1... Discriminator Loss: 1.2308... Generator Loss: 0.6104
Epoch 0/1... Discriminator Loss: 0.9405... Generator Loss: 0.8703
Epoch 0/1... Discriminator Loss: 0.9801... Generator Loss: 0.9940
Epoch 0/1... Discriminator Loss: 0.7992... Generator Loss: 1.5540
Epoch 0/1... Discriminator Loss: 0.8327... Generator Loss: 1.4186
Epoch 0/1... Discriminator Loss: 1.0318... Generator Loss: 0.7107
Epoch 0/1... Discriminator Loss: 0.8859... Generator Loss: 0.8412
Epoch 0/1... Discriminator Loss: 1.0396... Generator Loss: 0.6682
Epoch 0/1... Discriminator Loss: 1.1039... Generator Loss: 0.6123
Epoch 0/1... Discriminator Loss: 1.2456... Generator Loss: 0.4726
Epoch 0/1... Discriminator Loss: 1.0156... Generator Loss: 0.7636
Epoch 0/1... Discriminator Loss: 0.6976... Generator Loss: 1.2557
Epoch 0/1... Discriminator Loss: 0.6777... Generator Loss: 1.2845
Epoch 0/1... Discriminator Loss: 1.0858... Generator Loss: 0.6692
Epoch 0/1... Discriminator Loss: 0.8495... Generator Loss: 0.8626
Epoch 0/1... Discriminator Loss: 1.1593... Generator Loss: 0.5882
Epoch 0/1... Discriminator Loss: 0.9048... Generator Loss: 1.4940
Epoch 0/1... Discriminator Loss: 1.2386... Generator Loss: 1.4386
Epoch 0/1... Discriminator Loss: 1.0843... Generator Loss: 1.8658
Epoch 0/1... Discriminator Loss: 1.0820... Generator Loss: 1.0647
Epoch 0/1... Discriminator Loss: 1.0990... Generator Loss: 0.6831
Epoch 0/1... Discriminator Loss: 1.1639... Generator Loss: 0.5670
Epoch 0/1... Discriminator Loss: 1.0741... Generator Loss: 0.5735
Epoch 0/1... Discriminator Loss: 0.9696... Generator Loss: 1.0920
Epoch 0/1... Discriminator Loss: 0.7269... Generator Loss: 1.3633
Epoch 0/1... Discriminator Loss: 1.0651... Generator Loss: 1.7707
Epoch 0/1... Discriminator Loss: 0.8189... Generator Loss: 1.1368
Epoch 0/1... Discriminator Loss: 1.0760... Generator Loss: 0.9144
Epoch 0/1... Discriminator Loss: 1.0347... Generator Loss: 0.8758
Epoch 0/1... Discriminator Loss: 0.9143... Generator Loss: 0.7793
Epoch 0/1... Discriminator Loss: 0.8565... Generator Loss: 0.8095
Epoch 0/1... Discriminator Loss: 0.7353... Generator Loss: 1.1736
Epoch 0/1... Discriminator Loss: 1.2629... Generator Loss: 0.4963
Epoch 0/1... Discriminator Loss: 0.9898... Generator Loss: 0.6620
Epoch 0/1... Discriminator Loss: 0.9592... Generator Loss: 1.2135
Epoch 0/1... Discriminator Loss: 0.7906... Generator Loss: 1.4530
Epoch 0/1... Discriminator Loss: 0.7849... Generator Loss: 1.7610
Epoch 0/1... Discriminator Loss: 0.8475... Generator Loss: 1.2572
Epoch 0/1... Discriminator Loss: 1.0951... Generator Loss: 0.6482
Epoch 0/1... Discriminator Loss: 0.9699... Generator Loss: 1.3691
Epoch 0/1... Discriminator Loss: 0.7157... Generator Loss: 1.1140
Epoch 0/1... Discriminator Loss: 0.7213... Generator Loss: 1.1778
Epoch 0/1... Discriminator Loss: 0.7793... Generator Loss: 1.1813
Epoch 0/1... Discriminator Loss: 1.0900... Generator Loss: 0.6006
Epoch 0/1... Discriminator Loss: 0.8782... Generator Loss: 1.5366
Epoch 0/1... Discriminator Loss: 0.8351... Generator Loss: 1.0456
Epoch 0/1... Discriminator Loss: 0.9869... Generator Loss: 0.9248
Epoch 0/1... Discriminator Loss: 1.2676... Generator Loss: 1.8059
Epoch 0/1... Discriminator Loss: 0.8952... Generator Loss: 1.1491
Epoch 0/1... Discriminator Loss: 0.9679... Generator Loss: 0.9656
Epoch 0/1... Discriminator Loss: 0.9204... Generator Loss: 0.7387
Epoch 0/1... Discriminator Loss: 1.4421... Generator Loss: 0.4419
Epoch 0/1... Discriminator Loss: 0.9119... Generator Loss: 0.8601
Epoch 0/1... Discriminator Loss: 0.9743... Generator Loss: 0.8582
Epoch 0/1... Discriminator Loss: 0.9156... Generator Loss: 1.4282
Epoch 0/1... Discriminator Loss: 1.0982... Generator Loss: 0.8415
Epoch 0/1... Discriminator Loss: 1.1238... Generator Loss: 0.5799
Epoch 0/1... Discriminator Loss: 1.5368... Generator Loss: 0.3997
Epoch 0/1... Discriminator Loss: 1.0968... Generator Loss: 0.6369
Epoch 0/1... Discriminator Loss: 0.7443... Generator Loss: 1.5324
Epoch 0/1... Discriminator Loss: 1.3142... Generator Loss: 0.4201
Epoch 0/1... Discriminator Loss: 1.3774... Generator Loss: 0.4062
Epoch 0/1... Discriminator Loss: 0.8647... Generator Loss: 1.2977
Epoch 0/1... Discriminator Loss: 1.2594... Generator Loss: 0.4890
Epoch 0/1... Discriminator Loss: 0.9167... Generator Loss: 0.9518
Epoch 0/1... Discriminator Loss: 0.7919... Generator Loss: 0.9128
Epoch 0/1... Discriminator Loss: 0.9557... Generator Loss: 0.8211
Epoch 0/1... Discriminator Loss: 0.6555... Generator Loss: 1.4379
Epoch 0/1... Discriminator Loss: 0.9682... Generator Loss: 0.9140
Epoch 0/1... Discriminator Loss: 0.9495... Generator Loss: 1.2517
Epoch 0/1... Discriminator Loss: 1.1383... Generator Loss: 0.5720
Epoch 0/1... Discriminator Loss: 1.1968... Generator Loss: 1.9086
Epoch 0/1... Discriminator Loss: 0.7773... Generator Loss: 1.1207
Epoch 0/1... Discriminator Loss: 0.9961... Generator Loss: 0.9260
Epoch 0/1... Discriminator Loss: 1.3840... Generator Loss: 0.6090
Epoch 0/1... Discriminator Loss: 0.9549... Generator Loss: 0.8250
Epoch 0/1... Discriminator Loss: 1.3126... Generator Loss: 1.4835
Epoch 0/1... Discriminator Loss: 0.8848... Generator Loss: 1.0260
Epoch 0/1... Discriminator Loss: 1.1103... Generator Loss: 1.4072
Epoch 0/1... Discriminator Loss: 1.0203... Generator Loss: 0.6690
Epoch 0/1... Discriminator Loss: 0.7132... Generator Loss: 1.4957
Epoch 0/1... Discriminator Loss: 1.0605... Generator Loss: 0.8269
Epoch 0/1... Discriminator Loss: 0.9919... Generator Loss: 0.6999
Epoch 0/1... Discriminator Loss: 1.1952... Generator Loss: 0.9212
Epoch 0/1... Discriminator Loss: 0.8649... Generator Loss: 0.8868
Epoch 0/1... Discriminator Loss: 1.3573... Generator Loss: 1.0084
Epoch 0/1... Discriminator Loss: 0.7842... Generator Loss: 1.3222
Epoch 0/1... Discriminator Loss: 0.9606... Generator Loss: 0.8552
Epoch 0/1... Discriminator Loss: 0.9549... Generator Loss: 0.9553
Epoch 0/1... Discriminator Loss: 1.1822... Generator Loss: 0.4768
Epoch 0/1... Discriminator Loss: 1.2334... Generator Loss: 1.4521
Epoch 0/1... Discriminator Loss: 1.4065... Generator Loss: 0.4163
Epoch 0/1... Discriminator Loss: 0.9522... Generator Loss: 0.9236
Epoch 0/1... Discriminator Loss: 1.1411... Generator Loss: 0.6787
Epoch 0/1... Discriminator Loss: 1.1397... Generator Loss: 0.7195
Epoch 0/1... Discriminator Loss: 1.2005... Generator Loss: 0.5034
Epoch 0/1... Discriminator Loss: 0.8265... Generator Loss: 1.4480
Epoch 0/1... Discriminator Loss: 1.0612... Generator Loss: 0.8409
Epoch 0/1... Discriminator Loss: 1.1619... Generator Loss: 0.6050
Epoch 0/1... Discriminator Loss: 0.8950... Generator Loss: 0.6957
Epoch 0/1... Discriminator Loss: 1.0018... Generator Loss: 0.8000
Epoch 0/1... Discriminator Loss: 1.0149... Generator Loss: 0.8976
Epoch 0/1... Discriminator Loss: 1.2743... Generator Loss: 0.5113
Epoch 0/1... Discriminator Loss: 0.9972... Generator Loss: 1.2463
Epoch 0/1... Discriminator Loss: 1.1193... Generator Loss: 0.6049
Epoch 0/1... Discriminator Loss: 0.8222... Generator Loss: 1.0263
Epoch 0/1... Discriminator Loss: 1.1881... Generator Loss: 0.5446
Epoch 0/1... Discriminator Loss: 1.2131... Generator Loss: 0.4875
Epoch 0/1... Discriminator Loss: 1.2213... Generator Loss: 1.4818
Epoch 0/1... Discriminator Loss: 1.0245... Generator Loss: 0.9238
Epoch 0/1... Discriminator Loss: 0.9238... Generator Loss: 1.0463
Epoch 0/1... Discriminator Loss: 1.1690... Generator Loss: 1.2615
Epoch 0/1... Discriminator Loss: 0.7746... Generator Loss: 1.6083
Epoch 0/1... Discriminator Loss: 0.8713... Generator Loss: 1.3622
Epoch 0/1... Discriminator Loss: 1.0275... Generator Loss: 0.7523
Epoch 0/1... Discriminator Loss: 0.9697... Generator Loss: 0.8226
Epoch 0/1... Discriminator Loss: 1.0754... Generator Loss: 0.7266
Epoch 0/1... Discriminator Loss: 1.1727... Generator Loss: 1.2139
Epoch 0/1... Discriminator Loss: 0.8606... Generator Loss: 1.0120
Epoch 0/1... Discriminator Loss: 1.1161... Generator Loss: 0.5724
Epoch 0/1... Discriminator Loss: 0.8418... Generator Loss: 1.1587
Epoch 0/1... Discriminator Loss: 1.0489... Generator Loss: 0.6287
Epoch 0/1... Discriminator Loss: 1.0659... Generator Loss: 0.9376
Epoch 0/1... Discriminator Loss: 1.1445... Generator Loss: 0.7255
Epoch 0/1... Discriminator Loss: 0.9159... Generator Loss: 1.2690
Epoch 0/1... Discriminator Loss: 0.8793... Generator Loss: 1.2988
Epoch 0/1... Discriminator Loss: 1.2850... Generator Loss: 0.4539
Epoch 0/1... Discriminator Loss: 0.7360... Generator Loss: 1.3375
Epoch 0/1... Discriminator Loss: 0.9024... Generator Loss: 1.1378
Epoch 0/1... Discriminator Loss: 0.8864... Generator Loss: 0.8379
Epoch 0/1... Discriminator Loss: 1.6482... Generator Loss: 0.3480
Epoch 0/1... Discriminator Loss: 1.1370... Generator Loss: 0.6986
Epoch 0/1... Discriminator Loss: 1.4317... Generator Loss: 0.5038
Epoch 0/1... Discriminator Loss: 1.0445... Generator Loss: 0.6931
Epoch 0/1... Discriminator Loss: 1.0156... Generator Loss: 1.1621
Epoch 0/1... Discriminator Loss: 1.4970... Generator Loss: 0.5453
Epoch 0/1... Discriminator Loss: 1.6074... Generator Loss: 0.3230
Epoch 0/1... Discriminator Loss: 0.9848... Generator Loss: 1.0665
Epoch 0/1... Discriminator Loss: 0.8125... Generator Loss: 1.4829
Epoch 0/1... Discriminator Loss: 1.0925... Generator Loss: 0.9374
Epoch 0/1... Discriminator Loss: 1.2974... Generator Loss: 0.4591
Epoch 0/1... Discriminator Loss: 0.8978... Generator Loss: 1.2229
Epoch 0/1... Discriminator Loss: 1.1787... Generator Loss: 0.5758
Epoch 0/1... Discriminator Loss: 0.9925... Generator Loss: 0.7864
Epoch 0/1... Discriminator Loss: 0.9886... Generator Loss: 0.7587
Epoch 0/1... Discriminator Loss: 1.0839... Generator Loss: 0.8954
Epoch 0/1... Discriminator Loss: 1.0747... Generator Loss: 0.7893
Epoch 0/1... Discriminator Loss: 1.1435... Generator Loss: 0.7541
Epoch 0/1... Discriminator Loss: 1.0186... Generator Loss: 0.6674
Epoch 0/1... Discriminator Loss: 0.8172... Generator Loss: 1.1130
Epoch 0/1... Discriminator Loss: 1.4048... Generator Loss: 0.4495
Epoch 0/1... Discriminator Loss: 0.9679... Generator Loss: 1.0849
Epoch 0/1... Discriminator Loss: 1.3710... Generator Loss: 0.4559
Epoch 0/1... Discriminator Loss: 1.2662... Generator Loss: 1.5765
Epoch 0/1... Discriminator Loss: 1.1376... Generator Loss: 0.6283
Epoch 0/1... Discriminator Loss: 0.9671... Generator Loss: 0.8016
Epoch 0/1... Discriminator Loss: 0.8804... Generator Loss: 1.1726
Epoch 0/1... Discriminator Loss: 1.0890... Generator Loss: 0.9836
Epoch 0/1... Discriminator Loss: 1.1515... Generator Loss: 0.8245
Epoch 0/1... Discriminator Loss: 1.2161... Generator Loss: 0.5134
Epoch 0/1... Discriminator Loss: 0.7663... Generator Loss: 1.2372
Epoch 0/1... Discriminator Loss: 0.9303... Generator Loss: 0.8830
Epoch 0/1... Discriminator Loss: 0.7396... Generator Loss: 1.0296
Epoch 0/1... Discriminator Loss: 1.0421... Generator Loss: 0.8351
Epoch 0/1... Discriminator Loss: 0.9489... Generator Loss: 0.9214
Epoch 0/1... Discriminator Loss: 0.8691... Generator Loss: 0.7871
Epoch 0/1... Discriminator Loss: 0.9564... Generator Loss: 1.1579
Epoch 0/1... Discriminator Loss: 0.9240... Generator Loss: 0.7413
Epoch 0/1... Discriminator Loss: 1.1298... Generator Loss: 0.6970
Epoch 0/1... Discriminator Loss: 1.0463... Generator Loss: 0.6414
Epoch 0/1... Discriminator Loss: 0.9674... Generator Loss: 1.0606
Epoch 0/1... Discriminator Loss: 0.8290... Generator Loss: 0.9598
Epoch 0/1... Discriminator Loss: 0.8458... Generator Loss: 1.0732
Epoch 0/1... Discriminator Loss: 0.8514... Generator Loss: 0.8057
Epoch 0/1... Discriminator Loss: 0.9149... Generator Loss: 1.4510
Epoch 0/1... Discriminator Loss: 1.1734... Generator Loss: 0.5236
Epoch 0/1... Discriminator Loss: 1.2795... Generator Loss: 0.5439
Epoch 0/1... Discriminator Loss: 1.0695... Generator Loss: 0.9573
Epoch 0/1... Discriminator Loss: 0.9865... Generator Loss: 1.2607
Epoch 0/1... Discriminator Loss: 1.0781... Generator Loss: 1.2666
Epoch 0/1... Discriminator Loss: 0.9974... Generator Loss: 1.0361
Epoch 0/1... Discriminator Loss: 1.0500... Generator Loss: 0.8108
Epoch 0/1... Discriminator Loss: 1.0391... Generator Loss: 0.6203
Epoch 0/1... Discriminator Loss: 1.1247... Generator Loss: 0.7300
Epoch 0/1... Discriminator Loss: 1.3833... Generator Loss: 0.5230
Epoch 0/1... Discriminator Loss: 0.9366... Generator Loss: 0.7471
Epoch 0/1... Discriminator Loss: 0.8621... Generator Loss: 1.1994
Epoch 0/1... Discriminator Loss: 1.0629... Generator Loss: 0.8418
Epoch 0/1... Discriminator Loss: 1.1940... Generator Loss: 0.6207
Epoch 0/1... Discriminator Loss: 1.2803... Generator Loss: 0.5207
Epoch 0/1... Discriminator Loss: 1.0447... Generator Loss: 1.2069
Epoch 0/1... Discriminator Loss: 1.1069... Generator Loss: 0.7480
Epoch 0/1... Discriminator Loss: 1.4601... Generator Loss: 0.3814
Epoch 0/1... Discriminator Loss: 1.2740... Generator Loss: 1.0103
Epoch 0/1... Discriminator Loss: 1.2513... Generator Loss: 0.4745

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.